Building better DBS with evolution

In deep brain stimulation (DBS), electrodes inserted deep within a patients’ brain deliver electric pulses that can alleviate symptoms of Parkinson’s disease or tremor or even of diseases such as obsessive-compulsive disorder. Yet, the stimuli are usually standard, with uniform frequencies and amplitudes. To design better stimuli, Brocker et al. built a model of Parkinson’s disease and used computational evolution to find a new temporal stimulation pattern. Application of this evolution-derived stimulation pattern in animal models and Parkinson’s disease patients verified that it was just as effective as the standard stimulation pattern but required significantly less overall energy. Because one drawback of DBS is the need for frequent risky surgeries to change the power source, less drain on the battery means healthier patients.

Abstract

Brain stimulation is a promising therapy for several neurological disorders, including Parkinson’s disease. Stimulation parameters are selected empirically and are limited to the frequency and intensity of stimulation. We varied the temporal pattern of deep brain stimulation to ameliorate symptoms in a parkinsonian animal model and in humans with Parkinson’s disease. We used model-based computational evolution to optimize the stimulation pattern. The optimized pattern produced symptom relief comparable to that from standard high-frequency stimulation (a constant rate of 130 or 185 Hz) and outperformed frequency-matched standard stimulation in a parkinsonian rat model and in patients. Both optimized and standard high-frequency stimulation suppressed abnormal oscillatory activity in the basal ganglia of rats and humans. The results illustrate the utility of model-based computational evolution of temporal patterns to increase the efficiency of brain stimulation in treating Parkinson’s disease and thereby reduce the energy required for successful treatment below that of current brain stimulation paradigms.